def __init__(self):
self.init = False
#Are we paused
self.pause = False
#training mode enabled
self.train = False
self.tracking = False
self.useMog = True
self.showBGDiff = False
self.lastTimeWarningSent = 1
self.alertInterval = 10000
#seperate bg weight ratio's
self.weightBG1 = 0.2
self.weightBG2 = 0.6
self.trained = {}
self.counter = 0
self.trainCouter = 0
self.erodeIter = 1
#gausion weight ratio
self.blurKp = 1
#cutoff threshold for BW image
self.cutOffThresh=30;
#what size to limit our bounding boxes too
self.sizeL = 4000
self.sizeM = 1500
#kernal size for erode and dilate
self.kernalH = 3
self.kernalW = 3
self.kernel = np.ones((self.kernalH,self.kernalW),'uint8')
#kernal size
self.rec = Recognizer()
self.currFrame = None
self.currFrameOpt = None
self.contours = []
#tracking will start after this many frames
self.start = 10
#track interval don't do tracking every frame
self.track_interval = 20
#Use MOG BG extractor
self.bgs = cv2.BackgroundSubtractorMOG(24*60, 1
, 0.8, 0.5)
self.svmTracker = SVM()
self.svmReady = True
self.twtr = TwitterAlert()
class UmbrellaTracker:
def __init__(self):
self.init = False
#Are we paused
self.pause = False
#training mode enabled
self.train = False
self.tracking = False
self.useMog = True
self.showBGDiff = False
self.lastTimeWarningSent = 1
self.alertInterval = 10000
#seperate bg weight ratio's
self.weightBG1 = 0.2
self.weightBG2 = 0.6
self.trained = {}
self.counter = 0
self.trainCouter = 0
self.erodeIter = 1
#gausion weight ratio
self.blurKp = 1
#cutoff threshold for BW image
self.cutOffThresh=30;
#what size to limit our bounding boxes too
self.sizeL = 4000
self.sizeM = 1500
#kernal size for erode and dilate
self.kernalH = 3
self.kernalW = 3
self.kernel = np.ones((self.kernalH,self.kernalW),'uint8')
#kernal size
self.rec = Recognizer()
self.currFrame = None
self.currFrameOpt = None
self.contours = []
#tracking will start after this many frames
self.start = 10
#track interval don't do tracking every frame
self.track_interval = 20
#Use MOG BG extractor
self.bgs = cv2.BackgroundSubtractorMOG(24*60, 1
, 0.8, 0.5)
self.svmTracker = SVM()
self.svmReady = True
self.twtr = TwitterAlert()
def onMouseClick(self,event, x, y, flags, param ):
#select which roi to train for features
if event == cv.CV_EVENT_LBUTTONDOWN:
if len(self.contours) > 0:
for cont in self.contours:
if self.isWithinBB(x,y,cont):
#x,y,w,h = cv2.boundingRect(cont)
box = cv2.boundingRect(cont)
if self.train:
print "Trainig for umbrella"
name = "./train/pos/%s.png"%self.trainCouter
self.trained[name] = box
cv2.imwrite("./train/pos/%s.png"%self.trainCouter,self.currFrame)
#self.rec.createTargetFeatures(self.getROI(self.currFrame,box),box)
#self.svmTracker.train(self.getROI(self.currFrame,box),np.float32)
#self.svmReady = True
else:
print "Trainig for None umbrella"
name = "./train/neg/%s.png"%self.trainCouter
self.trained[name] = box
cv2.imwrite("./train/neg/%s.png"%self.trainCouter,self.currFrame)
self.trainCouter = self.trainCouter + 1
else:
print "wait for contours to get initialized"
def isWithinBB(self,x,y,cont):
#check weather a point clicked on screen is within a bounding box defined by contours
xb,yb,wb,hb = cv2.boundingRect(cont)
xbC = xb + wb
ybC = yb + hb
if x > xb and x <xbC:
if y > yb and y < ybC:
return True
return False
def getROI(self,image,box):
#cut out and return the section from an image extraceted from a contour
if len(box) == 4:
#make sure we have for courners seems sometimes we don'e #paranoid
x,y,w,h = box
return image[y:y+h,x:x+w]
return None
#get Video stream
def addGausianBlur(self,f1,level):
return cv2.GaussianBlur(f1,(level,level),1)
#get diff
def subtractFrames(self,f1,f2):
dif = cv2.absdiff(f1,f2)
#dif = cv2.bitwise_and(f1,f2)
return dif
#get image threshold to remove some noise
def getThreshold(self,f,cutOffThreshVal):
return cv2.threshold(f,cutOffThreshVal,255,0)
#find the contours in the image
def findCountoursBW(self,f):
conts,hier = cv2.findContours(f,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
#check size of countours ignore ones that are too small or large
return conts
def drawContours(self,image,conts,sizeL,sizeM,color):
#draw contors on image
for cnt in conts:
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(image,(x,y),(x+w,y+h), color,2)
return image
def counterSizeFilter(self,contours,sizeL,sizeM):
#filter out contours that fit a certain size
filtered = []
for cnt in contours:
x,y,w,h = cv2.boundingRect(cnt)
area = float(w)*float(h)
if area < sizeL and area > sizeM:
filtered.append(cnt)
return filtered
def drawContour(self,image,cont,color):
#TODO create a version that which works for single contours
x,y,w,h = cv2.boundingRect(cont)
cv2.rectangle(image,(x,y),(x+w,y+h), color,2)
return image
def drawContours2(self,image,conts,sizeL,sizeM,color):
#draw contors on image given array od contours in contour
for cnt in conts:
x,y,w,h = cnt[0],cnt[1],cnt[2],cnt[3]
#area = float(w)*float(h)
#if area < sizeL and area > sizeM:
cv2.rectangle(image,(x,y),(x+w,y+h), color,2)
return image
def drawBox(self,image,x,y,size,color):
cv2.rectangle(image,(x,y),(x+size,y+size),color)
return image
#this really slows things down!
def makeBW(self,f):
bwImg = cv2.cvtColor(f,cv.CV_RGB2GRAY)
return bwImg
def createTrainigOutput(self):
count = 1
fpP = open("info.dat","w")
fpN = open("bg.txt","w")
for key in self.trained.keys():
if key.find("neg") > 0:
output = "%s \n"%(key)
fpN.write(output)
else:
output = "%s %s %s %s %s %s \n"%(key,count,self.trained[key][0],self.trained[key][1],self.trained[key][2],self.trained[key][3])
fpP.write(output)
fpP.close()
fpN.close()
#Home Brew BG removal
def bgSmoothing(self,frameBG,arr1, arr2):
frameBG = cv2.equalizeHist(frameBG)
#blurring helps to normalize and smooth out noise
arr1 = self.addGausianBlur(arr1,self.blurKp)
arr2 = self.addGausianBlur(arr2,self.blurKp)
#add to wighted averages of the background in order to obtained weighterd average to remove noise
cv2.accumulateWeighted(frameBG,arr1,self.weightBG1)
cv2.accumulateWeighted(frameBG,arr2,self.weightBG2)
#normalize
res1 = cv2.convertScaleAbs(arr1)
res2 = cv2.convertScaleAbs(arr2)
#res1 = self.makeBW(res1)
#res2 = self.makeBW(res2)
return res1,res2
def bgSmooothing2(self,frame):
#OpenCV BG removal using MOG
#frameBW = self.makeBW(frame)
frame = self.addGausianBlur(frame,self.blurKp)
frame = cv2.equalizeHist(frame)
fgMask = self.bgs.apply(frame)
return fgMask
def track(self,f,vid):
f = self.makeBW(f)
#create numpy arrays from image frames
avg1 = np.float32(f)
avg2 = np.float32(f)
objs = []
while 1:
if not self.pause:
#get frame from video
_,f = vid.read()
f = self.makeBW(f)
res2 = f.copy()
#make BW first
if self.useMog:
mask = self.bgSmooothing2(res2)
else:
res1,res2 = self.bgSmoothing(f,avg1,avg2)
#get diff
mask = self.subtractFrames(res1,res2)
#lets threshold the image
_,mask = self.getThreshold(mask,self.cutOffThresh)
#Dilate and erode
#res3 = cv2.dilate(res3,kernel,iterations=3)
#res3 = cv2.erode(res3,kernel,iterations=2)
res3 = cv2.erode(mask,self.kernel,iterations=self.erodeIter)
#set this for later use
self.currFrame = res2
self.currFrameOpt = res3
cimage = np.copy(res3)
#find countours
if self.counter > self.start:
self.contours = self.findCountoursBW(cimage)
self.contours = self.counterSizeFilter(self.contours,self.sizeL,self.sizeM)
#make it color again
#lets do some interesting stuff
if len(self.contours) > 0 :
for cont in self.contours:
if self.counter % self.track_interval == 0:
box = self.getROI(res2,cv2.boundingRect(cont))
#self.rec.findUmbrellas(box)
#dont search every frame
if self.tracking and self.counter % self.track_interval == 0:
for thing in self.contours:
objs = self.rec.detectUmbrellaCascade(box)
umb = thing
if len(objs) > 0:
if self.lastTimeWarningSent == 1 or self.counter - self.lastTimeWarningSent > self.alertInterval:
print "Umbrella Detected!!!! run for cover!!!!"
self.twtr.sendAlert()
self.lastTimeWarningSent = self.counter
else:
print "Just sent an alert so won't send one again but just FYI there is an umbrella"
res2 = cv2.cvtColor(res2,cv2.COLOR_GRAY2BGR)
if len(objs) > 0:
res2 = self.drawContour(res2,umb,(255,0,0))
res2 = self.drawContours(res2,self.contours,self.sizeL,self.sizeM,(255,255,0))
#res3 = cv2.dilate(res3,kernel)
self.counter = self.counter + 1
#cv2.imwrite("./pngs/image-"+str(counter).zfill(5)+".png", mask)
#self.rec.getFeatures(res2,2)
#res1 = self.rec.drawFeatures(res2)
if len(self.rec.toTrack) > 0 and self.svmReady:
for person in self.contours:
#masks = self.rec.flannMatching(cv2.boundingRect(person))
matches = self.rec.trackObjects(res2)
#res3 = self.drawBox()
if self.showBGDiff:
cv2.imshow('bg1',res3)
cv2.imshow('bg2',res2)
if not self.init:
cv2.setMouseCallback('bg2',self.onMouseClick,None);
init = True
#break if esc is hit
k = cv2.waitKey(20)
if k == ord('t'):
if self.train:
print "training for Negative samples"
self.train = False
else:
print "training for positive samples"
self.train = True
if k == ord('d'):
if len(self.trained.keys()):
print "creating training output file.."
self.createTrainigOutput()
print "output training files ready"
self.trained = {}
self.trainCouter = 0
if k == ord('r'):
self.rec.reset()
if k == ord(' '):
print "paused"
self.pause = not self.pause
if k == ord('g'):
self.tracking = not self.tracking
print "Tracking:%s"%self.tracking
if k == ord('b'):
self.useMog = not self.useMog
print "MOG bg extraction %s"%self.useMog
if k == ord('x'):
self.showBGDiff = not self.showBGDiff
print "enable bg diff:%s"%self.showBGDiff
if k == ord('+'):
self.erodeIter = self.erodeIter + 1
print "erode : %s"%self.erodeIter
if k == ord('-') and self.erodeIter > 0:
self.erodeIter = self.erodeIter -1
print "erode : %s"%self.erodeIter
if k == 27:
break
